Identification and Classification of Mental Patterns of Farmers with Multifunctional Farms Regarding Extension of Multifunctional Agriculture

Document Type : Research Paper


1 PhD, Department of Agricultural Extension, Faculty of Agricultural Economics and Development University of Tehran, Karaj, Iran.

2 Assistant Professor, Department of Agricultural Extension & Education, Faculty of Agricultural Economics and Development, University of Tehran, Karaj, Iran.

3 Assistant Professor, Department of Agricultural Extension & Education, Faculty of Agricultural and Natural Resources, Higher Educational Complex of Saravan, Saravan, Iran.


Multifunctional agriculture as a new paradigm for agriculture and rural development has been considered by the world as a response to social, economic, and environmental challenges. Current mixed method research used the Q-method to examine and explain farmers’ mental patterns regarding  extension of multifunctional agriculture. In the qualitative step, the purposeful and snowball sampling methods were used to interview 11 academic experts. Analyzing data collected with MAXQDA12 software led to the extraction of 61 statements. 48 statements were selected as the Q sample. In the quantitative step, after collecting data from sorted Q statements by 16 farmers with multifunctional farms (N= 21), this data was analyzed using Q factor analysis in SPSS25 software. The result showed that 16 farmers surveyed had four mental patterns regarding extension of multifunctional agriculture, including Proponents of understanding the importance and benefits of multifunctional agriculture and limitations of productivist agriculture, Proponents of multifunctional agriculture facilitation and encouragement policies and extension and education factor, Proponents of farmers’ entrepreneurial skills and community demand for multifunctional agriculture, and Proponents of farm characteristics. These patterns account for 84.20% of the total variance. The first mental pattern (26.86%) and the second mental pattern (23.51%) explained a greater share of the total variance.


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